The present section is intended to introduce the reader to various aspects of art, which may be related to various aspects of the present principles that are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present principles. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of prior art.
Images taken with both digital cameras and conventional film cameras will pick up noise from a variety of sources. Further use of these pictures will often require that the noise be (partially) removed—for aesthetic purposes as in artistic work or marketing, or for practical purposes such as computer vision.
In salt and pepper noise (sparse light and dark disturbances), pixels in the picture are very different in color or intensity from their surrounding pixels; the defining characteristic is that the value of a noisy pixel bears no relation to the color of surrounding pixels. Generally this type of noise will only affect a small number of image pixels. When viewed, the image contains dark and white dots, hence the term salt and pepper noise. Typical sources include flecks of dust inside the camera and overheated or faulty CCD elements
In Gaussian noise, each pixel in the picture will be changed from its original value by a (usually) small amount. A histogram, a plot of the amount of distortion of a pixel value against the frequency with which it occurs, shows a normal distribution of noise. While other distributions are possible, the Gaussian (normal) distribution is usually a good model, due to the central limit theorem that says that the sum of different noises tends to approach a Gaussian distribution.
In either case, the noise at different pixels can be either correlated or uncorrelated; in many cases, noise values at different pixels are modeled as being independent and identically distributed, and hence uncorrelated.
Reducing noise in a picture requires selecting a noise reduction method.
In selecting a noise reduction algorithm, one would usually weigh several factors:                the available computer power and time available: a digital camera must apply noise reduction in a fraction of a second using a tiny onboard CPU, while a desktop computer has much more power and time and        whether sacrificing some real detail is acceptable if it allows more noise to be removed (how aggressively to decide whether variations in the image are noise or not).        
The noise reduction method in accordance with the present principles aims to reduce the visibility of noise and grain without overly smoothing picture details and edges.